{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8e4ee61a-28b5-4dbf-9157-3851658363d8",
   "metadata": {},
   "source": [
    "Plotting a timeline panel for Figure 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1da8ac5d-4072-46fa-9184-673367f564ef",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8d3c432f-4606-460a-848b-511e4dcff5a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_style('white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3dd35d68-be27-4318-8e5a-46d667113af2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = '../data/'\n",
    "metadata_path = '../data/metadata/'\n",
    "plot_path = '../figures/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bf86389-1b17-48a1-89f7-e9aef0ff5802",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Read data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef01450e-1058-40ea-ae8f-eebe992a25ef",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "##### Participants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "df8d3a59-5a11-4a30-9c65-dacad78eb256",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Subject ID</th>\n",
       "      <th>Date of Birth</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Birth Height (cm)</th>\n",
       "      <th>Birth Height Percentile</th>\n",
       "      <th>Birth Weight (kg)</th>\n",
       "      <th>Birth Weight Percentile</th>\n",
       "      <th>Birth Head Cir. (cm)</th>\n",
       "      <th>Birth Head Cir. Percentile</th>\n",
       "      <th>Race #1</th>\n",
       "      <th>...</th>\n",
       "      <th>Amphibian/Reptile</th>\n",
       "      <th>Bird</th>\n",
       "      <th>Other</th>\n",
       "      <th>Live on Farm?</th>\n",
       "      <th>Smokers at home?</th>\n",
       "      <th>Any medical conditions/signs/symptoms prior to study?</th>\n",
       "      <th>Condition #1</th>\n",
       "      <th>Past or Current?</th>\n",
       "      <th>Condition #2</th>\n",
       "      <th>Past or Current?.1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PrimaryKey</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Baby101</th>\n",
       "      <td>101</td>\n",
       "      <td>2018-01-24</td>\n",
       "      <td>Male</td>\n",
       "      <td>Not Documented</td>\n",
       "      <td>Not Documented</td>\n",
       "      <td>3.646</td>\n",
       "      <td>Not Documented</td>\n",
       "      <td>Not Documented</td>\n",
       "      <td>Not Documented</td>\n",
       "      <td>White/Caucasian</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Nevus</td>\n",
       "      <td>Current</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby102</th>\n",
       "      <td>102</td>\n",
       "      <td>2018-02-20</td>\n",
       "      <td>Male</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>3.35</td>\n",
       "      <td>37</td>\n",
       "      <td>34.5</td>\n",
       "      <td>25</td>\n",
       "      <td>Arab/North African</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby103</th>\n",
       "      <td>103</td>\n",
       "      <td>2018-02-21</td>\n",
       "      <td>Female</td>\n",
       "      <td>52</td>\n",
       "      <td>84</td>\n",
       "      <td>3.41</td>\n",
       "      <td>51</td>\n",
       "      <td>36.5</td>\n",
       "      <td>85</td>\n",
       "      <td>White/Caucasian</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>broken collar bone</td>\n",
       "      <td>Current</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby104</th>\n",
       "      <td>104</td>\n",
       "      <td>2018-03-12</td>\n",
       "      <td>Female</td>\n",
       "      <td>55.2</td>\n",
       "      <td>98</td>\n",
       "      <td>3.615</td>\n",
       "      <td>61</td>\n",
       "      <td>35.6</td>\n",
       "      <td>64</td>\n",
       "      <td>White/Caucasian</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby105</th>\n",
       "      <td>105</td>\n",
       "      <td>2018-03-30</td>\n",
       "      <td>Female</td>\n",
       "      <td>50.8</td>\n",
       "      <td>72.31</td>\n",
       "      <td>3.97</td>\n",
       "      <td>89.09</td>\n",
       "      <td>34</td>\n",
       "      <td>32.73</td>\n",
       "      <td>White/Caucasian</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pigs</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Jaundice</td>\n",
       "      <td>Current</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Subject ID Date of Birth  Gender Birth Height (cm)  \\\n",
       "PrimaryKey                                                       \n",
       "Baby101            101    2018-01-24    Male    Not Documented   \n",
       "Baby102            102    2018-02-20    Male                50   \n",
       "Baby103            103    2018-02-21  Female                52   \n",
       "Baby104            104    2018-03-12  Female              55.2   \n",
       "Baby105            105    2018-03-30  Female              50.8   \n",
       "\n",
       "           Birth Height Percentile Birth Weight (kg) Birth Weight Percentile  \\\n",
       "PrimaryKey                                                                     \n",
       "Baby101             Not Documented             3.646          Not Documented   \n",
       "Baby102                         50              3.35                      37   \n",
       "Baby103                         84              3.41                      51   \n",
       "Baby104                         98             3.615                      61   \n",
       "Baby105                      72.31              3.97                   89.09   \n",
       "\n",
       "           Birth Head Cir. (cm) Birth Head Cir. Percentile  \\\n",
       "PrimaryKey                                                   \n",
       "Baby101          Not Documented             Not Documented   \n",
       "Baby102                    34.5                         25   \n",
       "Baby103                    36.5                         85   \n",
       "Baby104                    35.6                         64   \n",
       "Baby105                      34                      32.73   \n",
       "\n",
       "                       Race #1  ... Amphibian/Reptile Bird  Other  \\\n",
       "PrimaryKey                      ...                                 \n",
       "Baby101        White/Caucasian  ...               NaN  NaN    NaN   \n",
       "Baby102     Arab/North African  ...               NaN  NaN    NaN   \n",
       "Baby103        White/Caucasian  ...               NaN  NaN    NaN   \n",
       "Baby104        White/Caucasian  ...               NaN  NaN    NaN   \n",
       "Baby105        White/Caucasian  ...               NaN  NaN   pigs   \n",
       "\n",
       "           Live on Farm? Smokers at home?  \\\n",
       "PrimaryKey                                  \n",
       "Baby101               No               No   \n",
       "Baby102               No               No   \n",
       "Baby103               No               No   \n",
       "Baby104               No               No   \n",
       "Baby105               No               No   \n",
       "\n",
       "           Any medical conditions/signs/symptoms prior to study?  \\\n",
       "PrimaryKey                                                         \n",
       "Baby101                                                   Yes      \n",
       "Baby102                                                    No      \n",
       "Baby103                                                   Yes      \n",
       "Baby104                                                    No      \n",
       "Baby105                                                   Yes      \n",
       "\n",
       "                  Condition #1 Past or Current? Condition #2  \\\n",
       "PrimaryKey                                                     \n",
       "Baby101                  Nevus          Current          NaN   \n",
       "Baby102                    NaN              NaN          NaN   \n",
       "Baby103     broken collar bone          Current          NaN   \n",
       "Baby104                    NaN              NaN          NaN   \n",
       "Baby105               Jaundice          Current          NaN   \n",
       "\n",
       "           Past or Current?.1  \n",
       "PrimaryKey                     \n",
       "Baby101                   NaN  \n",
       "Baby102                   NaN  \n",
       "Baby103                   NaN  \n",
       "Baby104                   NaN  \n",
       "Baby105                   NaN  \n",
       "\n",
       "[5 rows x 70 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "participant_data = pd.read_csv(os.path.join(metadata_path, 'participants.tsv'), sep='\\t', index_col=0)\n",
    "participant_data['Date of Birth'] = pd.to_datetime(participant_data['Date of Birth'])\n",
    "participant_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2049118-d187-408a-86c5-856377458a9a",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "##### Visits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6eb3c595-4ab4-4fbd-81cd-bae1c93af484",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BabyN</th>\n",
       "      <th>Subject</th>\n",
       "      <th>Type of Visit</th>\n",
       "      <th>Visit #</th>\n",
       "      <th>Visit Date</th>\n",
       "      <th>Age (Days)</th>\n",
       "      <th>Height (cm)</th>\n",
       "      <th>Height Percentile</th>\n",
       "      <th>Weight (kg)</th>\n",
       "      <th>Weight Percentile</th>\n",
       "      <th>...</th>\n",
       "      <th>Today HiB</th>\n",
       "      <th>Today Prevnar 13</th>\n",
       "      <th>Today Hepatitis B</th>\n",
       "      <th>Today MMR</th>\n",
       "      <th>Today Varicella</th>\n",
       "      <th>Today Hepatitis A</th>\n",
       "      <th>Today Influenza (Flu)</th>\n",
       "      <th>Today Rotavirus</th>\n",
       "      <th>Today Other</th>\n",
       "      <th>Comment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PrimaryKey</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Baby101_V3</th>\n",
       "      <td>Baby101</td>\n",
       "      <td>101</td>\n",
       "      <td>Enrollment, 2wk</td>\n",
       "      <td>V3</td>\n",
       "      <td>2018-02-16</td>\n",
       "      <td>23</td>\n",
       "      <td>55</td>\n",
       "      <td>65</td>\n",
       "      <td>4.423</td>\n",
       "      <td>64</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby102_V1</th>\n",
       "      <td>Baby102</td>\n",
       "      <td>102</td>\n",
       "      <td>Enrollment, &lt;1wk</td>\n",
       "      <td>V1</td>\n",
       "      <td>2018-02-26</td>\n",
       "      <td>6</td>\n",
       "      <td>50.8</td>\n",
       "      <td>46</td>\n",
       "      <td>3.23</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby103_V1</th>\n",
       "      <td>Baby103</td>\n",
       "      <td>103</td>\n",
       "      <td>Enrollment, &lt;1wk</td>\n",
       "      <td>V1</td>\n",
       "      <td>2018-02-26</td>\n",
       "      <td>5</td>\n",
       "      <td>52.1</td>\n",
       "      <td>82</td>\n",
       "      <td>3.416</td>\n",
       "      <td>41</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby104_V2</th>\n",
       "      <td>Baby104</td>\n",
       "      <td>104</td>\n",
       "      <td>Enrollment, 1wk</td>\n",
       "      <td>V2</td>\n",
       "      <td>2018-03-23</td>\n",
       "      <td>11</td>\n",
       "      <td>Not Documented</td>\n",
       "      <td>Not Documented</td>\n",
       "      <td>3.812</td>\n",
       "      <td>67</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby105_V1</th>\n",
       "      <td>Baby105</td>\n",
       "      <td>105</td>\n",
       "      <td>Enrollment, &lt;1wk</td>\n",
       "      <td>V1</td>\n",
       "      <td>2018-04-02</td>\n",
       "      <td>3</td>\n",
       "      <td>51.4</td>\n",
       "      <td>75.48</td>\n",
       "      <td>3.884</td>\n",
       "      <td>81.17</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 114 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              BabyN  Subject     Type of Visit Visit #  Visit Date  \\\n",
       "PrimaryKey                                                           \n",
       "Baby101_V3  Baby101      101   Enrollment, 2wk      V3  2018-02-16   \n",
       "Baby102_V1  Baby102      102  Enrollment, <1wk      V1  2018-02-26   \n",
       "Baby103_V1  Baby103      103  Enrollment, <1wk      V1  2018-02-26   \n",
       "Baby104_V2  Baby104      104   Enrollment, 1wk      V2  2018-03-23   \n",
       "Baby105_V1  Baby105      105  Enrollment, <1wk      V1  2018-04-02   \n",
       "\n",
       "            Age (Days)     Height (cm) Height Percentile Weight (kg)  \\\n",
       "PrimaryKey                                                             \n",
       "Baby101_V3          23              55                65       4.423   \n",
       "Baby102_V1           6            50.8                46        3.23   \n",
       "Baby103_V1           5            52.1                82       3.416   \n",
       "Baby104_V2          11  Not Documented    Not Documented       3.812   \n",
       "Baby105_V1           3            51.4             75.48       3.884   \n",
       "\n",
       "           Weight Percentile  ... Today HiB Today Prevnar 13  \\\n",
       "PrimaryKey                    ...                              \n",
       "Baby101_V3                64  ...       NaN              NaN   \n",
       "Baby102_V1                20  ...       NaN              NaN   \n",
       "Baby103_V1                41  ...       NaN              NaN   \n",
       "Baby104_V2                67  ...       NaN              NaN   \n",
       "Baby105_V1             81.17  ...       NaN              NaN   \n",
       "\n",
       "           Today Hepatitis B Today MMR Today Varicella Today Hepatitis A  \\\n",
       "PrimaryKey                                                                 \n",
       "Baby101_V3               NaN       NaN             NaN               NaN   \n",
       "Baby102_V1               NaN       NaN             NaN               NaN   \n",
       "Baby103_V1               NaN       NaN             NaN               NaN   \n",
       "Baby104_V2               NaN       NaN             NaN               NaN   \n",
       "Baby105_V1               NaN       NaN             NaN               NaN   \n",
       "\n",
       "           Today Influenza (Flu) Today Rotavirus Today Other Comment  \n",
       "PrimaryKey                                                            \n",
       "Baby101_V3                   NaN             NaN         NaN     NaN  \n",
       "Baby102_V1                   NaN             NaN         NaN     NaN  \n",
       "Baby103_V1                   NaN             NaN         NaN     NaN  \n",
       "Baby104_V2                   NaN             NaN         NaN     NaN  \n",
       "Baby105_V1                   NaN             NaN         NaN     NaN  \n",
       "\n",
       "[5 rows x 114 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visit_data = pd.read_csv(os.path.join(metadata_path, 'visits.tsv'), sep='\\t', index_col=0)\n",
    "visit_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5ac1d9b-93ba-46fd-981d-54d95c551f5e",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "##### Antibiotics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "afbd7f6e-f507-4b80-ab62-f15d55c16b6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BabyN</th>\n",
       "      <th>AntibioticN</th>\n",
       "      <th>Name</th>\n",
       "      <th>Reason</th>\n",
       "      <th>Start_Date</th>\n",
       "      <th>End_Date</th>\n",
       "      <th>Duration_(days)</th>\n",
       "      <th>Route</th>\n",
       "      <th>Age_at_start</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PrimaryKey</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Baby134_Antibiotic1</th>\n",
       "      <td>Baby134</td>\n",
       "      <td>1</td>\n",
       "      <td>piperacillin/ tazobactam</td>\n",
       "      <td>R/O sepsis</td>\n",
       "      <td>2018-11-26</td>\n",
       "      <td>2018-11-28</td>\n",
       "      <td>2.0</td>\n",
       "      <td>IV</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby134_Antibiotic2</th>\n",
       "      <td>Baby134</td>\n",
       "      <td>2</td>\n",
       "      <td>ampicillin</td>\n",
       "      <td>R/O sepsis</td>\n",
       "      <td>2018-11-26</td>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>IV</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby134_Antibiotic3</th>\n",
       "      <td>Baby134</td>\n",
       "      <td>3</td>\n",
       "      <td>gentamicin</td>\n",
       "      <td>R/O sepsis</td>\n",
       "      <td>2018-11-26</td>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>IV</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby134_Antibiotic4</th>\n",
       "      <td>Baby134</td>\n",
       "      <td>4</td>\n",
       "      <td>vancomycin</td>\n",
       "      <td>R/O sepsis</td>\n",
       "      <td>2018-11-26</td>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>IV</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby235_Antibiotic1</th>\n",
       "      <td>Baby235</td>\n",
       "      <td>1</td>\n",
       "      <td>unknown anitbiotic(s)</td>\n",
       "      <td>R/O sepsis, later confirmed neg.</td>\n",
       "      <td>2018-06-19</td>\n",
       "      <td>Not Documented</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       BabyN  AntibioticN                      Name  \\\n",
       "PrimaryKey                                                            \n",
       "Baby134_Antibiotic1  Baby134            1  piperacillin/ tazobactam   \n",
       "Baby134_Antibiotic2  Baby134            2                ampicillin   \n",
       "Baby134_Antibiotic3  Baby134            3                gentamicin   \n",
       "Baby134_Antibiotic4  Baby134            4                vancomycin   \n",
       "Baby235_Antibiotic1  Baby235            1     unknown anitbiotic(s)   \n",
       "\n",
       "                                               Reason Start_Date  \\\n",
       "PrimaryKey                                                         \n",
       "Baby134_Antibiotic1                        R/O sepsis 2018-11-26   \n",
       "Baby134_Antibiotic2                        R/O sepsis 2018-11-26   \n",
       "Baby134_Antibiotic3                        R/O sepsis 2018-11-26   \n",
       "Baby134_Antibiotic4                        R/O sepsis 2018-11-26   \n",
       "Baby235_Antibiotic1  R/O sepsis, later confirmed neg. 2018-06-19   \n",
       "\n",
       "                           End_Date  Duration_(days) Route  Age_at_start  \n",
       "PrimaryKey                                                                \n",
       "Baby134_Antibiotic1      2018-11-28              2.0    IV           4.0  \n",
       "Baby134_Antibiotic2      2018-11-27              1.0    IV           4.0  \n",
       "Baby134_Antibiotic3      2018-11-27              1.0    IV           4.0  \n",
       "Baby134_Antibiotic4      2018-11-27              1.0    IV           4.0  \n",
       "Baby235_Antibiotic1  Not Documented              NaN   NaN           6.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abx_usage = pd.read_csv(os.path.join(metadata_path, 'antibiotic_usage.tsv'), sep='\\t', index_col=0)\n",
    "\n",
    "# Add age at start, calculated from start data and date of birth\n",
    "abx_usage['Duration_(days)'] = pd.to_numeric(abx_usage['Duration_(days)'], errors='coerce')\n",
    "abx_usage['Start_Date'] = pd.to_datetime(abx_usage['Start_Date'], errors='coerce')\n",
    "abx_usage['Age_at_start'] = [(row['Start_Date'] - participant_data.loc[row['BabyN'], 'Date of Birth']).days for i, row in abx_usage.iterrows()]\n",
    "abx_usage.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "566ba6f6-0465-4778-b288-752d4508d7c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BabyN</th>\n",
       "      <th>AntibioticN</th>\n",
       "      <th>Name</th>\n",
       "      <th>Reason</th>\n",
       "      <th>Start_Date</th>\n",
       "      <th>End_Date</th>\n",
       "      <th>Duration_(days)</th>\n",
       "      <th>Route</th>\n",
       "      <th>Age_at_start</th>\n",
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       "    <tr>\n",
       "      <th>PrimaryKey</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Baby103_Antibiotic1</th>\n",
       "      <td>Baby103</td>\n",
       "      <td>1</td>\n",
       "      <td>amoxicillin</td>\n",
       "      <td>Pneumonia</td>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>10.0</td>\n",
       "      <td>oral</td>\n",
       "      <td>685.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby106_Antibiotic2</th>\n",
       "      <td>Baby106</td>\n",
       "      <td>2</td>\n",
       "      <td>amoxicillin</td>\n",
       "      <td>AOM</td>\n",
       "      <td>2019-01-16</td>\n",
       "      <td>2019-01-26</td>\n",
       "      <td>10.0</td>\n",
       "      <td>oral</td>\n",
       "      <td>295.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby107_Antibiotic2</th>\n",
       "      <td>Baby107</td>\n",
       "      <td>2</td>\n",
       "      <td>amoxicillin</td>\n",
       "      <td>OME</td>\n",
       "      <td>2018-12-12</td>\n",
       "      <td>2018-12-22</td>\n",
       "      <td>10.0</td>\n",
       "      <td>oral</td>\n",
       "      <td>254.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby107_Antibiotic3</th>\n",
       "      <td>Baby107</td>\n",
       "      <td>3</td>\n",
       "      <td>amoxicillin</td>\n",
       "      <td>bronchiolitis</td>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>2019-01-11</td>\n",
       "      <td>7.0</td>\n",
       "      <td>oral</td>\n",
       "      <td>277.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby107_Antibiotic4</th>\n",
       "      <td>Baby107</td>\n",
       "      <td>4</td>\n",
       "      <td>cefdinir</td>\n",
       "      <td>bronchilitis, pneumonia</td>\n",
       "      <td>2019-01-11</td>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>3.0</td>\n",
       "      <td>oral</td>\n",
       "      <td>284.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby259_Antibiotic1</th>\n",
       "      <td>Baby259</td>\n",
       "      <td>1</td>\n",
       "      <td>amoxicillin</td>\n",
       "      <td>AOM, URI</td>\n",
       "      <td>2019-04-21</td>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>oral</td>\n",
       "      <td>177.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby261_Antibiotic1</th>\n",
       "      <td>Baby261</td>\n",
       "      <td>1</td>\n",
       "      <td>amoxicillin</td>\n",
       "      <td>Bronchiolitis</td>\n",
       "      <td>2019-04-08</td>\n",
       "      <td>2019-04-13</td>\n",
       "      <td>5.0</td>\n",
       "      <td>oral</td>\n",
       "      <td>158.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby261_Antibiotic2</th>\n",
       "      <td>Baby261</td>\n",
       "      <td>2</td>\n",
       "      <td>amoxicillin clavulanate</td>\n",
       "      <td>pyoderma</td>\n",
       "      <td>2019-10-21</td>\n",
       "      <td>2019-10-28</td>\n",
       "      <td>7.0</td>\n",
       "      <td>oral</td>\n",
       "      <td>354.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby261_Antibiotic5</th>\n",
       "      <td>Baby261</td>\n",
       "      <td>5</td>\n",
       "      <td>amoxicillin</td>\n",
       "      <td>AOM</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>2020-02-09</td>\n",
       "      <td>10.0</td>\n",
       "      <td>oral</td>\n",
       "      <td>455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby261_Antibiotic6</th>\n",
       "      <td>Baby261</td>\n",
       "      <td>6</td>\n",
       "      <td>amoxicillin clavulanate</td>\n",
       "      <td>dog bite, had stitches</td>\n",
       "      <td>2020-04-26</td>\n",
       "      <td>2020-05-04</td>\n",
       "      <td>8.0</td>\n",
       "      <td>oral</td>\n",
       "      <td>542.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       BabyN  AntibioticN                     Name  \\\n",
       "PrimaryKey                                                           \n",
       "Baby103_Antibiotic1  Baby103            1              amoxicillin   \n",
       "Baby106_Antibiotic2  Baby106            2              amoxicillin   \n",
       "Baby107_Antibiotic2  Baby107            2              amoxicillin   \n",
       "Baby107_Antibiotic3  Baby107            3              amoxicillin   \n",
       "Baby107_Antibiotic4  Baby107            4                 cefdinir   \n",
       "...                      ...          ...                      ...   \n",
       "Baby259_Antibiotic1  Baby259            1              amoxicillin   \n",
       "Baby261_Antibiotic1  Baby261            1              amoxicillin   \n",
       "Baby261_Antibiotic2  Baby261            2  amoxicillin clavulanate   \n",
       "Baby261_Antibiotic5  Baby261            5              amoxicillin   \n",
       "Baby261_Antibiotic6  Baby261            6  amoxicillin clavulanate   \n",
       "\n",
       "                                      Reason Start_Date    End_Date  \\\n",
       "PrimaryKey                                                            \n",
       "Baby103_Antibiotic1                Pneumonia 2020-01-07  2020-01-17   \n",
       "Baby106_Antibiotic2                      AOM 2019-01-16  2019-01-26   \n",
       "Baby107_Antibiotic2                      OME 2018-12-12  2018-12-22   \n",
       "Baby107_Antibiotic3            bronchiolitis 2019-01-04  2019-01-11   \n",
       "Baby107_Antibiotic4  bronchilitis, pneumonia 2019-01-11  2019-01-14   \n",
       "...                                      ...        ...         ...   \n",
       "Baby259_Antibiotic1                 AOM, URI 2019-04-21  2019-05-01   \n",
       "Baby261_Antibiotic1            Bronchiolitis 2019-04-08  2019-04-13   \n",
       "Baby261_Antibiotic2                 pyoderma 2019-10-21  2019-10-28   \n",
       "Baby261_Antibiotic5                      AOM 2020-01-30  2020-02-09   \n",
       "Baby261_Antibiotic6   dog bite, had stitches 2020-04-26  2020-05-04   \n",
       "\n",
       "                     Duration_(days) Route  Age_at_start  \n",
       "PrimaryKey                                                \n",
       "Baby103_Antibiotic1             10.0  oral         685.0  \n",
       "Baby106_Antibiotic2             10.0  oral         295.0  \n",
       "Baby107_Antibiotic2             10.0  oral         254.0  \n",
       "Baby107_Antibiotic3              7.0  oral         277.0  \n",
       "Baby107_Antibiotic4              3.0  oral         284.0  \n",
       "...                              ...   ...           ...  \n",
       "Baby259_Antibiotic1             10.0  oral         177.0  \n",
       "Baby261_Antibiotic1              5.0  oral         158.0  \n",
       "Baby261_Antibiotic2              7.0  oral         354.0  \n",
       "Baby261_Antibiotic5             10.0  oral         455.0  \n",
       "Baby261_Antibiotic6              8.0  oral         542.0  \n",
       "\n",
       "[165 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter for oral abx only\n",
    "abx_usage_oral = abx_usage[abx_usage.Route=='oral']\n",
    "# filter out instances without known timings\n",
    "abx_usage_oral.Age_at_start.isnull().sum() # only one of these\n",
    "abx_usage_oral = abx_usage_oral[~abx_usage_oral.Age_at_start.isnull()]\n",
    "abx_usage_oral"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bfd8fbd-a7b3-49bb-a99f-41782b0c9070",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "##### Samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ef80876a-6a1e-42ad-b270-4d2f642c7417",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(709, 26)\n"
     ]
    },
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       "      <th>SubmissionType</th>\n",
       "      <th>SampleNumber</th>\n",
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       "      <td>...</td>\n",
       "      <td>2 months</td>\n",
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       "      <td>204_V5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Home</td>\n",
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       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>Human Infant</td>\n",
       "      <td>MetaG</td>\n",
       "      <td>226</td>\n",
       "      <td>...</td>\n",
       "      <td>1-6 days</td>\n",
       "      <td>NaN</td>\n",
       "      <td>226_V1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Clinic</td>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>107_V3</td>\n",
       "      <td>Primary in Tube</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Box 7, A3</td>\n",
       "      <td>Stool</td>\n",
       "      <td>Human Infant</td>\n",
       "      <td>MetaG</td>\n",
       "      <td>107</td>\n",
       "      <td>...</td>\n",
       "      <td>2 weeks</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107_V3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Home</td>\n",
       "      <td>3350273</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>108_V3</td>\n",
       "      <td>Primary in Tube</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Box 7, A4</td>\n",
       "      <td>Stool</td>\n",
       "      <td>Human Infant</td>\n",
       "      <td>MetaG</td>\n",
       "      <td>108</td>\n",
       "      <td>...</td>\n",
       "      <td>2 weeks</td>\n",
       "      <td>NaN</td>\n",
       "      <td>108_V3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Home</td>\n",
       "      <td>5095831</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>109_V1</td>\n",
       "      <td>Primary in Tube</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Box 7, A5</td>\n",
       "      <td>Stool</td>\n",
       "      <td>Human Infant</td>\n",
       "      <td>MetaG</td>\n",
       "      <td>109</td>\n",
       "      <td>...</td>\n",
       "      <td>1-6 days</td>\n",
       "      <td>NaN</td>\n",
       "      <td>109_V1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Home</td>\n",
       "      <td>4963525</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  SampleID   SubmissionType  SampleNumber  SampleIDValidation  \\\n",
       "0   204_V5  Primary in Tube             1                 NaN   \n",
       "1   226_V1  Primary in Tube             2                 NaN   \n",
       "2   107_V3  Primary in Tube             3                 NaN   \n",
       "3   108_V3  Primary in Tube             4                 NaN   \n",
       "4   109_V1  Primary in Tube             5                 NaN   \n",
       "\n",
       "  DiversigenCheckInSampleName BoxLocation SampleType  SampleSource  \\\n",
       "0                       204_S   Box 7, A1      Stool  Human Infant   \n",
       "1                         NaN   Box 7, A2      Stool  Human Infant   \n",
       "2                         NaN   Box 7, A3      Stool  Human Infant   \n",
       "3                         NaN   Box 7, A4      Stool  Human Infant   \n",
       "4                         NaN   Box 7, A5      Stool  Human Infant   \n",
       "\n",
       "  SequencingType  BabyN  ...  VisitCodeTimeEquivalent  \\\n",
       "0          MetaG    204  ...                 2 months   \n",
       "1          MetaG    226  ...                 1-6 days   \n",
       "2          MetaG    107  ...                  2 weeks   \n",
       "3          MetaG    108  ...                  2 weeks   \n",
       "4          MetaG    109  ...                 1-6 days   \n",
       "\n",
       "  VisitCode_or_TimeEquivalent_checked  OutputFileName Plate  Row Column  \\\n",
       "0                                 NaN          204_V5     3    3      8   \n",
       "1                                 NaN          226_V1     5    5      2   \n",
       "2                                 NaN          107_V3     7    3      7   \n",
       "3                                 NaN          108_V3     3    1      1   \n",
       "4                                 NaN          109_V1     6    6     12   \n",
       "\n",
       "  age_at_collection  collection_method    Count  gt_2.5  \n",
       "0              67.0               Home  3628514    True  \n",
       "1               4.0             Clinic  3363490    True  \n",
       "2              18.0               Home  3350273    True  \n",
       "3              16.0               Home  5095831    True  \n",
       "4               3.0               Home  4963525    True  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stool_samples = pd.read_csv(os.path.join(metadata_path, 'stool', 'stool_metadata.csv'))\n",
    "print(stool_samples.shape)\n",
    "stool_samples.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "17725351-e27a-44c8-b44b-6e33d5cecc86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1008, 23)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SubmissionType</th>\n",
       "      <th>SampleNumber</th>\n",
       "      <th>SampleID</th>\n",
       "      <th>SampleIDValidation</th>\n",
       "      <th>DiversigenCheckInSampleName</th>\n",
       "      <th>ReplacesLowVolumeSampleID</th>\n",
       "      <th>BoxLocation</th>\n",
       "      <th>SampleType</th>\n",
       "      <th>SampleSource</th>\n",
       "      <th>SequencingType</th>\n",
       "      <th>...</th>\n",
       "      <th>DOB_checked</th>\n",
       "      <th>CollectionDate</th>\n",
       "      <th>CollectionDate_checked</th>\n",
       "      <th>VisitCode</th>\n",
       "      <th>VisitCode_checked</th>\n",
       "      <th>SwabCode</th>\n",
       "      <th>OutputFileName</th>\n",
       "      <th>Plate</th>\n",
       "      <th>Row</th>\n",
       "      <th>Column</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Primary in Tube</td>\n",
       "      <td>1</td>\n",
       "      <td>103_V5_NS_A1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Box 1, A1</td>\n",
       "      <td>Nasal Swab</td>\n",
       "      <td>Human Infant</td>\n",
       "      <td>16S</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-04-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>V5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A1</td>\n",
       "      <td>103_V5_NS_A1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Primary in Tube</td>\n",
       "      <td>2</td>\n",
       "      <td>106_V5_NS_A1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Box 1, A3</td>\n",
       "      <td>Nasal Swab</td>\n",
       "      <td>Human Infant</td>\n",
       "      <td>16S</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-05-29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>V5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A1</td>\n",
       "      <td>106_V5_NS_A1</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Primary in Tube</td>\n",
       "      <td>3</td>\n",
       "      <td>107_V2_NS_A1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Box 1, A4</td>\n",
       "      <td>Nasal Swab</td>\n",
       "      <td>Human Infant</td>\n",
       "      <td>16S</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-04-09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>V2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A1</td>\n",
       "      <td>107_V2_NS_A1</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Primary in Tube</td>\n",
       "      <td>4</td>\n",
       "      <td>107_V3_NS_A1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107_V8_NS_A1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Box 1, A5</td>\n",
       "      <td>Nasal Swab</td>\n",
       "      <td>Human Infant</td>\n",
       "      <td>16S</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-04-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>V3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A1</td>\n",
       "      <td>107_V3_NS_A1</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Primary in Tube</td>\n",
       "      <td>5</td>\n",
       "      <td>107_V5_NS_A1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Box 1, A8</td>\n",
       "      <td>Nasal Swab</td>\n",
       "      <td>Human Infant</td>\n",
       "      <td>16S</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-06-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>V5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A1</td>\n",
       "      <td>107_V5_NS_A1</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    SubmissionType  SampleNumber      SampleID  SampleIDValidation  \\\n",
       "0  Primary in Tube             1  103_V5_NS_A1                 NaN   \n",
       "1  Primary in Tube             2  106_V5_NS_A1                 NaN   \n",
       "2  Primary in Tube             3  107_V2_NS_A1                 NaN   \n",
       "3  Primary in Tube             4  107_V3_NS_A1                 NaN   \n",
       "4  Primary in Tube             5  107_V5_NS_A1                 NaN   \n",
       "\n",
       "  DiversigenCheckInSampleName ReplacesLowVolumeSampleID BoxLocation  \\\n",
       "0                         NaN                       NaN   Box 1, A1   \n",
       "1                         NaN                       NaN   Box 1, A3   \n",
       "2                         NaN                       NaN   Box 1, A4   \n",
       "3                107_V8_NS_A1                       NaN   Box 1, A5   \n",
       "4                         NaN                       NaN   Box 1, A8   \n",
       "\n",
       "   SampleType  SampleSource SequencingType  ...  DOB_checked  CollectionDate  \\\n",
       "0  Nasal Swab  Human Infant            16S  ...          NaN      2018-04-27   \n",
       "1  Nasal Swab  Human Infant            16S  ...          NaN      2018-05-29   \n",
       "2  Nasal Swab  Human Infant            16S  ...          NaN      2018-04-09   \n",
       "3  Nasal Swab  Human Infant            16S  ...          NaN      2018-04-20   \n",
       "4  Nasal Swab  Human Infant            16S  ...          NaN      2018-06-04   \n",
       "\n",
       "  CollectionDate_checked  VisitCode VisitCode_checked  SwabCode  \\\n",
       "0                    NaN         V5               NaN        A1   \n",
       "1                    NaN         V5               NaN        A1   \n",
       "2                    NaN         V2               NaN        A1   \n",
       "3                    NaN         V3               NaN        A1   \n",
       "4                    NaN         V5               NaN        A1   \n",
       "\n",
       "  OutputFileName  Plate Row Column  \n",
       "0   103_V5_NS_A1      4   6     12  \n",
       "1   106_V5_NS_A1      8   3      4  \n",
       "2   107_V2_NS_A1     11   2      2  \n",
       "3   107_V3_NS_A1     11   2      3  \n",
       "4   107_V5_NS_A1     11   2      4  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nasal_samples = pd.read_csv(os.path.join(metadata_path, 'nasal', 'nasal_metadata.csv'))\n",
    "print(nasal_samples.shape)\n",
    "nasal_samples.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d096169d-2a4a-4048-91be-efa428540b1d",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "##### Titres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "482e593a-3882-4a4c-9cea-f9f90ce71ffc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BabyN</th>\n",
       "      <th>Date</th>\n",
       "      <th>VisitCode</th>\n",
       "      <th>VisitType</th>\n",
       "      <th>Age(Days)</th>\n",
       "      <th>Category</th>\n",
       "      <th>CollectionMethod</th>\n",
       "      <th>SampleType</th>\n",
       "      <th>Antigen</th>\n",
       "      <th>Value</th>\n",
       "      <th>Unit</th>\n",
       "      <th>ProtectiveThreshold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PrimaryKey</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Baby106_2m_PT</th>\n",
       "      <td>Baby106</td>\n",
       "      <td>2018-05-29</td>\n",
       "      <td>V5</td>\n",
       "      <td>Well Check 02m</td>\n",
       "      <td>63</td>\n",
       "      <td>2m</td>\n",
       "      <td>Heelstick</td>\n",
       "      <td>Serum</td>\n",
       "      <td>PT</td>\n",
       "      <td>5.00</td>\n",
       "      <td>ELU/ml</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby110_2m_PT</th>\n",
       "      <td>Baby110</td>\n",
       "      <td>2018-07-09</td>\n",
       "      <td>V5</td>\n",
       "      <td>Well Check 02m</td>\n",
       "      <td>63</td>\n",
       "      <td>2m</td>\n",
       "      <td>Heelstick</td>\n",
       "      <td>Serum</td>\n",
       "      <td>PT</td>\n",
       "      <td>60.00</td>\n",
       "      <td>ELU/ml</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby113_2m_PT</th>\n",
       "      <td>Baby113</td>\n",
       "      <td>2018-07-26</td>\n",
       "      <td>V5</td>\n",
       "      <td>Well Check 02m</td>\n",
       "      <td>72</td>\n",
       "      <td>2m</td>\n",
       "      <td>Heelstick</td>\n",
       "      <td>Serum</td>\n",
       "      <td>PT</td>\n",
       "      <td>22.00</td>\n",
       "      <td>ELU/ml</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby113_2m_Dip</th>\n",
       "      <td>Baby113</td>\n",
       "      <td>2018-07-26</td>\n",
       "      <td>V5</td>\n",
       "      <td>Well Check 02m</td>\n",
       "      <td>72</td>\n",
       "      <td>2m</td>\n",
       "      <td>Heelstick</td>\n",
       "      <td>Serum</td>\n",
       "      <td>Dip</td>\n",
       "      <td>0.58</td>\n",
       "      <td>IU/ml</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby115_2m_PT</th>\n",
       "      <td>Baby115</td>\n",
       "      <td>2018-08-24</td>\n",
       "      <td>V6</td>\n",
       "      <td>Well Check 04m</td>\n",
       "      <td>92</td>\n",
       "      <td>2m</td>\n",
       "      <td>Heelstick</td>\n",
       "      <td>Serum</td>\n",
       "      <td>PT</td>\n",
       "      <td>5.00</td>\n",
       "      <td>ELU/ml</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  BabyN        Date VisitCode       VisitType  Age(Days)  \\\n",
       "PrimaryKey                                                                 \n",
       "Baby106_2m_PT   Baby106  2018-05-29        V5  Well Check 02m         63   \n",
       "Baby110_2m_PT   Baby110  2018-07-09        V5  Well Check 02m         63   \n",
       "Baby113_2m_PT   Baby113  2018-07-26        V5  Well Check 02m         72   \n",
       "Baby113_2m_Dip  Baby113  2018-07-26        V5  Well Check 02m         72   \n",
       "Baby115_2m_PT   Baby115  2018-08-24        V6  Well Check 04m         92   \n",
       "\n",
       "               Category CollectionMethod SampleType Antigen  Value    Unit  \\\n",
       "PrimaryKey                                                                   \n",
       "Baby106_2m_PT        2m        Heelstick      Serum      PT   5.00  ELU/ml   \n",
       "Baby110_2m_PT        2m        Heelstick      Serum      PT  60.00  ELU/ml   \n",
       "Baby113_2m_PT        2m        Heelstick      Serum      PT  22.00  ELU/ml   \n",
       "Baby113_2m_Dip       2m        Heelstick      Serum     Dip   0.58   IU/ml   \n",
       "Baby115_2m_PT        2m        Heelstick      Serum      PT   5.00  ELU/ml   \n",
       "\n",
       "                ProtectiveThreshold  \n",
       "PrimaryKey                           \n",
       "Baby106_2m_PT                   8.0  \n",
       "Baby110_2m_PT                   8.0  \n",
       "Baby113_2m_PT                   8.0  \n",
       "Baby113_2m_Dip                  0.1  \n",
       "Baby115_2m_PT                   8.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titre_data = pd.read_csv(os.path.join(data_path, 'vaccine_response', 'titers.tsv'), sep='\\t', index_col=0)\n",
    "titre_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bd37e38-8e38-447a-aef4-a5d6b486911f",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "##### Vaccinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aeaa73b8-4d0e-4559-8d33-b3be793ef650",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BabyN</th>\n",
       "      <th>Date of Birth</th>\n",
       "      <th>Visit Date</th>\n",
       "      <th>Age (Days)</th>\n",
       "      <th>Vaccine</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PrimaryKey</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101_DTaP_1</th>\n",
       "      <td>101</td>\n",
       "      <td>1/24/18</td>\n",
       "      <td>3/26/18</td>\n",
       "      <td>61</td>\n",
       "      <td>DTaP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102_DTaP_1</th>\n",
       "      <td>102</td>\n",
       "      <td>2/20/18</td>\n",
       "      <td>4/20/18</td>\n",
       "      <td>59</td>\n",
       "      <td>DTaP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102_DTaP_2</th>\n",
       "      <td>102</td>\n",
       "      <td>2/20/18</td>\n",
       "      <td>6/20/18</td>\n",
       "      <td>120</td>\n",
       "      <td>DTaP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103_DTaP_1</th>\n",
       "      <td>103</td>\n",
       "      <td>2/21/18</td>\n",
       "      <td>4/27/18</td>\n",
       "      <td>65</td>\n",
       "      <td>DTaP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103_DTaP_2</th>\n",
       "      <td>103</td>\n",
       "      <td>2/21/18</td>\n",
       "      <td>8/24/18</td>\n",
       "      <td>184</td>\n",
       "      <td>DTaP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            BabyN Date of Birth Visit Date  Age (Days) Vaccine\n",
       "PrimaryKey                                                    \n",
       "101_DTaP_1    101       1/24/18    3/26/18          61    DTaP\n",
       "102_DTaP_1    102       2/20/18    4/20/18          59    DTaP\n",
       "102_DTaP_2    102       2/20/18    6/20/18         120    DTaP\n",
       "103_DTaP_1    103       2/21/18    4/27/18          65    DTaP\n",
       "103_DTaP_2    103       2/21/18    8/24/18         184    DTaP"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine_data = pd.read_csv(os.path.join(metadata_path, 'vaccines.tsv'), sep='\\t', index_col=0)\n",
    "vaccine_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "888585a1-c3d8-4ad3-a766-c2a10a3bfd80",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Set up for plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fba95113-6d80-40d7-b383-13efd072d6b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Subject ID</th>\n",
       "      <th>enrollment_age</th>\n",
       "      <th>final_visit_age</th>\n",
       "      <th>final_sample_age</th>\n",
       "      <th>final_age</th>\n",
       "      <th>y_pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>226</td>\n",
       "      <td>4</td>\n",
       "      <td>881.0</td>\n",
       "      <td>881.0</td>\n",
       "      <td>881.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>223</td>\n",
       "      <td>6</td>\n",
       "      <td>867.0</td>\n",
       "      <td>867.0</td>\n",
       "      <td>867.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>103</td>\n",
       "      <td>5</td>\n",
       "      <td>804.0</td>\n",
       "      <td>804.0</td>\n",
       "      <td>804.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>210</td>\n",
       "      <td>11</td>\n",
       "      <td>802.0</td>\n",
       "      <td>802.0</td>\n",
       "      <td>802.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>112</td>\n",
       "      <td>15</td>\n",
       "      <td>802.0</td>\n",
       "      <td>802.0</td>\n",
       "      <td>802.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101</td>\n",
       "      <td>23</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>213</td>\n",
       "      <td>6</td>\n",
       "      <td>53.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>104</td>\n",
       "      <td>11</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>242</td>\n",
       "      <td>12</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>262</td>\n",
       "      <td>7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Subject ID  enrollment_age  final_visit_age  final_sample_age  final_age  \\\n",
       "61         226               4            881.0             881.0      881.0   \n",
       "58         223               6            867.0             867.0      867.0   \n",
       "2          103               5            804.0             804.0      804.0   \n",
       "45         210              11            802.0             802.0      802.0   \n",
       "11         112              15            802.0             802.0      802.0   \n",
       "..         ...             ...              ...               ...        ...   \n",
       "0          101              23             61.0              61.0       61.0   \n",
       "48         213               6             53.0              55.0       55.0   \n",
       "3          104              11             16.0              11.0       16.0   \n",
       "77         242              12             12.0              12.0       12.0   \n",
       "97         262               7              7.0               7.0        7.0   \n",
       "\n",
       "    y_pos  \n",
       "61      0  \n",
       "58      1  \n",
       "2       2  \n",
       "45      3  \n",
       "11      4  \n",
       "..    ...  \n",
       "0      96  \n",
       "48     97  \n",
       "3      98  \n",
       "77     99  \n",
       "97    100  \n",
       "\n",
       "[101 rows x 6 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# figure out the length of the line for each infant\n",
    "# this is from enrollment visit to last visit\n",
    "time_in_study = participant_data[['Subject ID']] # start the table\n",
    "# add enrollment age\n",
    "enrollment = visit_data[visit_data['Type of Visit'].str.contains('Enrollment')][['Subject','Age (Days)']].rename(columns={'Subject':'Subject ID'})\n",
    "time_in_study = time_in_study.merge(enrollment).rename(columns={'Age (Days)':'enrollment_age'})\n",
    "# add final visit age - loop through each kid, must be a better way though, groupby on visit_data probably\n",
    "for i,row in time_in_study.iterrows():\n",
    "    sid = row['Subject ID']\n",
    "    maxage = visit_data[visit_data.Subject==sid]['Age (Days)'].max()\n",
    "    time_in_study.loc[i, 'final_visit_age'] = maxage\n",
    "    # also add final sample age - might be older? \n",
    "    # stool \n",
    "    cd = pd.to_datetime(stool_samples[stool_samples['BabyN']==sid].CollectionDate)\n",
    "    bd = pd.to_datetime(stool_samples[stool_samples['BabyN']==sid].DOB)\n",
    "    maxage_stool = (cd-bd).dt.days.max()\n",
    "    # nasal\n",
    "    cd = pd.to_datetime(nasal_samples[nasal_samples['BabyN']==sid].CollectionDate)\n",
    "    bd = pd.to_datetime(nasal_samples[nasal_samples['BabyN']==sid].DOB)\n",
    "    maxage_nasal = (cd-bd).dt.days.max()\n",
    "    time_in_study.loc[i,'final_sample_age'] = np.nanmax([maxage_stool, maxage_nasal])\n",
    "    # add final final (!) age\n",
    "    time_in_study.loc[i,'final_age'] = np.nanmax([maxage, maxage_stool, maxage_nasal])\n",
    "\n",
    "# sort kids by final age\n",
    "time_in_study.sort_values('final_age', inplace=True, ascending=False)\n",
    "# add y-position for this kid for plotting\n",
    "time_in_study['y_pos'] = range(time_in_study.shape[0])\n",
    "time_in_study"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26eac6c4-715c-4a81-914f-74223e9265e6",
   "metadata": {},
   "source": [
    "### Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "24299a70-673a-42f6-8b3c-e83698b642d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# marker properties\n",
    "titer_shape = '1'\n",
    "titer_colour = 'k'\n",
    "titer_size = 70 # do not understand this marker size scale\n",
    "\n",
    "vax_shape = '|'\n",
    "vax_colour = 'k'\n",
    "\n",
    "stool_shape = 'o'\n",
    "stool_colour = 'tab:blue'\n",
    "stool_alpha = 0.5\n",
    "\n",
    "nasal_shape = 'o'\n",
    "nasal_colour = 'tab:red'\n",
    "nasal_alpha = 0.5\n",
    "\n",
    "abx_colour = 'tab:green'\n",
    "abx_width = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "40555fa0-e660-4025-827b-d5a904311d25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make a legend in a separate file\n",
    "legend_elements = [mpl.lines.Line2D([0],[0], \n",
    "                                    color=abx_colour, lw=abx_width,\n",
    "                                    label='Oral antibiotic'),\n",
    "                   mpl.lines.Line2D([0],[0],lw=0,\n",
    "                                    color=vax_colour, marker=vax_shape, ms=10,\n",
    "                                    label='Vaccination (DTaP/Hib/Prevnar13)'),\n",
    "                   mpl.lines.Line2D([0],[0],lw=0, \n",
    "                                    color=titer_colour, marker=titer_shape, ms=15, \n",
    "                                    label='Antibody titer measurement'),\n",
    "                   mpl.lines.Line2D([0],[0],lw=0,\n",
    "                                    color=stool_colour, marker=stool_shape,\n",
    "                                    label='Stool sample'),\n",
    "                   mpl.lines.Line2D([0],[0],lw=0,\n",
    "                                    color=nasal_colour, marker=nasal_shape,\n",
    "                                    label='Nasal sample'),\n",
    "                  ]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.legend(handles=legend_elements)\n",
    "ax.axis('off')\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(plot_path, 'F1_timeline_legend.pdf'), dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5fc3d826-a001-40e5-a7b1-decbb706330a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# actual timeline plot\n",
    "fig = plt.figure(figsize=(10,12))\n",
    "ax = fig.add_subplot(111)\n",
    "# add vertical lines for 2m, 4m, 6m, 1 year\n",
    "[ax.axvline(d, color='k', lw=0.5) for d in [60, 120, 180, 365, 365*2]]\n",
    "# add lines for each kid -- from time_in_study\n",
    "for index,row in time_in_study.iterrows():\n",
    "    ax.plot([row.enrollment_age,row.final_age],[row.y_pos,row.y_pos],\n",
    "            color='tab:grey', lw=0.5)\n",
    "# add where oral abx was taken\n",
    "for index, row in time_in_study.iterrows():\n",
    "    temp = abx_usage_oral[abx_usage_oral.BabyN=='Baby'+str(int(row['Subject ID']))]\n",
    "    for abxi, abxrow in temp.iterrows():\n",
    "        # draw a bar for each of these\n",
    "        ax.plot([abxrow.Age_at_start, abxrow.Age_at_start+abxrow['Duration_(days)']],\n",
    "                [row.y_pos,row.y_pos],\n",
    "                color=abx_colour,\n",
    "                lw=abx_width)\n",
    "# add stool samples\n",
    "for index, row in time_in_study.iterrows():\n",
    "    temp = stool_samples[stool_samples.BabyN==row['Subject ID']]\n",
    "    if temp.shape[0] >= 1:\n",
    "        cd = pd.to_datetime(temp.CollectionDate)\n",
    "        bd = pd.to_datetime(temp.DOB)\n",
    "        xtp = (cd-bd).dt.days\n",
    "        ax.scatter(xtp, [row.y_pos]*len(xtp),\n",
    "                   color=stool_colour,\n",
    "                   marker=stool_shape,\n",
    "                   alpha=nasal_alpha,\n",
    "                   lw=0)\n",
    "# add nasal samples\n",
    "for index, row in time_in_study.iterrows():\n",
    "    temp = nasal_samples[nasal_samples.BabyN==row['Subject ID']]\n",
    "    if temp.shape[0] >= 1:\n",
    "        cd = pd.to_datetime(temp.CollectionDate)\n",
    "        bd = pd.to_datetime(temp.DOB)\n",
    "        xtp = (cd-bd).dt.days\n",
    "        ax.scatter(xtp, [row.y_pos]*len(xtp),\n",
    "                   color=nasal_colour,\n",
    "                   marker=nasal_shape,\n",
    "                   alpha=nasal_alpha,\n",
    "                   lw=0)\n",
    "# add titer measurements\n",
    "for index,row in time_in_study.iterrows():\n",
    "    temp = titre_data[titre_data.BabyN=='Baby'+str(int(row['Subject ID']))]\n",
    "    if temp.shape[0] >= 1:\n",
    "        xtp = temp['Age(Days)'].unique() # days that there are titre measurements at\n",
    "        ax.scatter(xtp, [row.y_pos]*len(xtp),\n",
    "                   color=titer_colour,\n",
    "                   marker=titer_shape,\n",
    "                   s=titer_size)\n",
    "# add vaccinations\n",
    "for index, row in time_in_study.iterrows():\n",
    "    temp = vaccine_data[vaccine_data.BabyN==row['Subject ID']]\n",
    "    # restrict to only the vaccines we're interested in \n",
    "    temp = temp[temp.Vaccine.isin(['DTaP','HiB','Prevnar 13'])]\n",
    "    if temp.shape[0] >= 1:\n",
    "        xtp = temp['Age (Days)'].unique()\n",
    "        ax.scatter(xtp, [row.y_pos]*len(xtp),\n",
    "                   color=vax_colour,\n",
    "                   marker=vax_shape)\n",
    "# tidy axes\n",
    "ax.set_xlim(-5,time_in_study.final_age.max()+5)\n",
    "ax.set_ylim(-1,time_in_study.y_pos.max()+0.5)\n",
    "ax.set_yticks([])\n",
    "[ax.spines[loc].set_visible(False) for loc in ['left','right','top']]\n",
    "ax.set_xlabel('Age (days)')\n",
    "# save figure\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(plot_path, 'F1_timeline.pdf'), dpi=600)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
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}
